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The system is based on a connection- ist large vocabulary speech recognizer and a probabilistic information retrieval system. We discuss the development of a realtime Broadcast News speech rec- ognizer, and its integration into an SDR system.
Two advances were made for this task: Precision and recall results using the Text Retrieval Conference TREC SDR evaluation infrastructure are reported throughout the paper, and we discuss the application of these developments to a large scale SDR task based on an archive of British English broadcast news.
It has been estimated that a large proportion of human-generated informa- tion is spoken and that much of this is in the form of television and radio broadcasts Morrison and Morrison While the navigation and retrieval of textual data is commonplace, it still an outstanding research problem to perform such operations on archives of spoken data.
The problem of broadcast news retrieval, in particular, has received considerable attention, not least because of the availability of acoustic training data, and the mix of acoustic conditions that characterize news broadcasts, including substantial sections of planned, noise-free speech.
Two principal approaches have been used for SDR: Since queries may be assumed to be at the word level,1 additional processing is required to build a suitable index from a phone-level transcription: To make phone-level approaches more robust, algorithms to scan phone lattices for keywords have been developed James and Young ; Foote et al.
Such lattice approaches are more suitable for applications with a fixed query eg, filtering or routingas each new query word de- mands that the entire archive of phone lattices is scanned—an operation which scales linearly with archive size.
These approaches—and the one described in this paper—all use a similar methodology.
English translations along with the documents in the original language. You may submit a translation from another ATA certified translator or . Even if you write every day, the type of English writing necessary for academic writing is a whole different beast (in other words, it’s completely different). It’s not the kind of writing you might use every day, like in blogs or in letters. Cross-language spoken document retrieval (CL-SDR) is the technology that facilitates automatic retrieval of relevant information from a collection of spoken documents in a language that is different from that used in the queries.
A large vocabulary speech recognizer is used to provide a word-level transcription; the transcribed audio segment is then treated as a text document by an information retrieval IR system. The advantages of the word-based approach are clear.
IR is more robust when applied to words compared with phone n-grams, particular at the high error rates ob- served when recognizing broadcast data although explicit modelling of phone recog- nition errors has been investigated by Wechsler et al.
Furthermore, word-level recognition enables the constraints of the pronunciation dic- tionary and language model to be applied. Two possible disadvantages of the word- based approach, compared with the phone-based approach, are an increased compu- tational burden and a closed vocabulary.
In section 3 we show that large vocabulary continuous speech recognition of broadcast speech demands around three times the computation compared with phone recognition, and can be achieved in real-time on a modern PC.
This paper summarizes the principal results pre- sented in some earlier conference papers Abberley et al. Our work has focussed on British English, with an archive constructed from around 2.
Access to this evaluation infrastructure has enabled us to evaluate algorithmic developments without having to develop a parallel evaluation framework for British English broadcast news. The paper is organized as follows.
Section 2 outlines the collection of application- specific acoustic and textual data for British English broadcast speech. The large vo- cabulary speech recognition system is described in section 3, with particular reference to the computationally efficient algorithms employed, the models required for British English broadcast news and evaluation of the speech recognition performance.
Sec- tion 4 describes the basic IR methods that we have used and section 5 describes the evaluation metrics employed.Students, professors, and researchers in every discipline use academic writing to convey ideas, make arguments, and engage in scholarly conversation. Academic writing is characterized by evidence-based arguments, precise word choice, logical organization, and an impersonal tone.
Though sometimes. Cross-language spoken document retrieval (CL-SDR) is the technology that facilitates automatic retrieval of relevant information from a collection of spoken documents in a language that is different from that used in the queries. Even if you write every day, the type of English writing necessary for academic writing is a whole different beast (in other words, it’s completely different).
It’s not the kind of writing you might use every day, like in blogs or in letters. High tech, human touch. That is the University of Twente.
Some 3, scientists and other professionals working together on cutting-edge research, innovations with real-world relevance and inspiring education for more than 9, students. English translations along with the documents in the original language. You may submit a translation from another ATA certified translator or .
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